This is the second version of the Google Landmarks dataset (GLDv2), which contains images annotated with labels representing human-made and natural landmarks. The dataset can be used for landmark recognition and retrieval experiments. This version of the dataset contains approximately 5 million images, split into 3 sets of images: train, index and test. The dataset was presented in our CVPR'20 paper. In this repository, we present download links for all dataset files and relevant code for metric computation. This dataset was associated to two Kaggle challenges, on landmark recognition and landmark retrieval. Results were discussed as part of a CVPR'19 workshop. In this repository, we also provide scores for the top 10 teams in the challenges, based on the latest ground-truth version. Please visit the challenge and workshop webpages for more details on the data, tasks and technical solutions from top teams.

Dataset

This dataset was created by Bartek Sadlej

Contents

Dataset Card for Dataset Name

  Dataset Summary

This dataset is a subset of Kaggle's Google Landmark Recognition 2021 competition with only the categories with more than 500 images. https://www.kaggle.com/competitions/landmark-recognition-2021/data The dataset consists of a total of 45579 224x224 color images in 51 categories.

  Languages

English

  Dataset Structure





  Data Fields

landmark_id: Int - Numeric identifier of the category category :… See the full description on the dataset page: https://huggingface.co/datasets/pemujo/GLDv2_Top_51_Categories.

About

Few days ago i was thinking to start some new project but couldn't find one that looks a bit exciting to me. So , then i found about facial landmarks , then i started to found some datasets for it . There were many datasets , but Flickr Dataset came out to be the best out of them with 70,000 images having 68 landmarks coefficients and as the size shows the data was a big too around 900 GB , so i decided to form a smaller version of it so that we are able to atleast work on such task. So i created this dataset.

The objective of creating this dataset is to predict keypoint positions on face images. This can be used as a building block in several applications, such as:

1. tracking faces in images and video
2. analysing facial expressions
3. detecting dysmorphic facial signs for medical diagnosis
4. biometrics / face recognition

Detecing facial keypoints is a very challenging problem. Facial features vary greatly from one individual to another, and even for a single individual, there is a large amount of variation due to 3D pose, size, position, viewing angle, and illumination conditions. Computer vision research has come a long way in addressing these difficulties, but there remain many opportunities for improvement.

Some Sample images

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2137176%2Fdb17e16db7aefd0848ca3acd99001262%2Fdownload.png?generation=1608374055920310&alt=media"> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2137176%2Fdfa119b710b9edb47f0f6b2326b4cbdd%2Fdownload_1.png?generation=1608374048827571&alt=media">

Actual Dataset can be seen at https://github.com/NVlabs/ffhq-dataset

Content

This dataset contains 6000 records in two files : 1. A json file having below format {'face_landmarks': [[191.5, 617.5],[210.5, 717.5], ...............], 'file_name': '00000.png'}

1. images folder , having the same names as provided in json file, cropped

Licenses

The individual images were published in Flickr by their respective authors under either Creative Commons BY 2.0, Creative Commons BY-NC 2.0, Public Domain Mark 1.0, Public Domain CC0 1.0, or U.S. Government Works license. All of these licenses allow free use, redistribution, and adaptation for non-commercial purposes. However, some of them require giving appropriate credit to the original author, as well as indicating any changes that were made to the images. The license and original author of each image are indicated in the metadata.

https://creativecommons.org/licenses/by/2.0/ https://creativecommons.org/licenses/by-nc/2.0/ https://creativecommons.org/publicdomain/mark/1.0/ https://creativecommons.org/publicdomain/zero/1.0/ http://www.usa.gov/copyright.shtml The dataset itself (including JSON metadata, download script, and documentation) is made available under Creative Commons BY-NC-SA 4.0 license by NVIDIA Corporation. You can use, redistribute, and adapt it for non-commercial purposes, as long as you (a) give appropriate credit by citing our paper, (b) indicate any changes that you've made, and (c) distribute any derivative works under the same license.

https://creativecommons.org/licenses/by-nc-sa/4.0/

News Regarding Updates

Its takes a lot of time and resources to generate this dataset in one run. So , i need to run it multiple times generating different subsets ,hence it takes a lot of time to complete it. Date : 19/12/2020 Currently it has 6000 images and respective metadata. Date : 19/12/2020 Currently it has 10000 images and respective metadata. Date : 23/12/2020 updated correctly it has 5000 images and respective metadata.

Dataset

This dataset was created by Mark Wijkhuizen

Contents

Dataset

This dataset was created by Manoj Prabhakar

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Manoj Prabhakar

Released under CC0: Public Domain

Contents

Create dataset

global_test_dataset = tf.keras.preprocessing.image_dataset_from_directory(BASE_DIR+'/test', label_mode=None, shuffle=False, batch_size=1, image_size=(224, 224))
filepath = [x[:-4] for x in map(os.path.basename, global_test_dataset.file_paths)]
filepath_ds = tf.data.Dataset.from_tensor_slices(filepath)
dev_test_dataset = tf.data.Dataset.zip((global_test_dataset.unbatch(), filepath_ds))
global_test_dataset_size = len(filepath)
print('test images: ', global_test_dataset_size)

with tf.io.TFRecordWriter('landmark-recognition-2021-test.tfrec') as file_writer:
  for img, path in tqdm(dev_test_dataset.as_numpy_iterator(), total=global_test_dataset_size):
    img = tf.cast(tf.image.resize(img, [224, 224], method='nearest'), 'uint8')
    img_jpeg = tf.io.encode_jpeg(img, quality=70, optimize_size=True).numpy()
    record_bytes = tf.train.Example(features=tf.train.Features(feature={
      'image': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_jpeg])),
      'id': tf.train.Feature(bytes_list=tf.train.BytesList(value=[path])),
    })).SerializeToString()
    file_writer.write(record_bytes)

Load dataset

def decode_tfrecord(record_bytes):
  features = tf.io.parse_single_example(record_bytes, {
    'image': tf.io.FixedLenFeature([], tf.string), 
    'id': tf.io.FixedLenFeature([], tf.string)
  })
  img = tf.io.decode_jpeg(features['image'])
  img = tf.reshape(img, [224, 224, 3])
  return {'image': img, 'id': features['id']}


FNAMES_TRAIN_TFRECORDS = np.sort(tf.io.gfile.glob(BASE_DIR+'/landmark-recognition-2021-test.tfrec'))
global_train_ds = tf.data.TFRecordDataset(FNAMES_TRAIN_TFRECORDS, num_parallel_reads=None)
global_train_ds = global_train_ds.map(decode_tfrecord, num_parallel_calls=AUTO)

Dataset Card for "hagrid-mediapipe-hands"

This dataset is designed to train a ControlNet with human hands. It includes hand landmarks detected by MediaPipe(for more information refer to: https://developers.google.com/mediapipe/solutions/vision/hand_landmarker). The source image data is from HaGRID dataset and we use a modified version from Kaggle(https://www.kaggle.com/datasets/innominate817/hagrid-classification-512p) to build this dataset. There are 507050 data samples in total… See the full description on the dataset page: https://huggingface.co/datasets/Vincent-luo/hagrid-mediapipe-hands.

Dataset

This dataset was created by kaggler

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Manoj Prabhakar

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Kumar Shubham

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by taroo

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by James Dietle

Contents

Dataset

This dataset was created by Mark Wijkhuizen

Contents

Dataset

This dataset was created by Mark Wijkhuizen

Contents

Dataset

This dataset was created by Saurav Joshi

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Kumar Shubham

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Inet Yoshi

Released under Data files © Original Authors

Contents

The dataset contains markings for pets' faces - dogs and cats. It contains bounding boxes and coordinates of eyes and nose. This dataset will be useful for applications that require finding an animal's face and aligning it with key points.

Wild data collected from petfinder and similar services

The training set contains data from the COCO dataset without pets in order to reduce false positives on examples that do not contain animals.

The dataset contains 25 thousand photos of animals.

The data was marked up with the help of grandingdino and manually filtered.

All markup in yolo format

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2474522%2Fca04ece69a92e7c28b12eea8c58c4224%2Fval_batch0_labels.jpg?generation=1746616983571796&alt=media" alt="">